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The Estimation of Continual Causal Effect for Dataset Shifting Streams

Baining Chen, Yiming Zhang, Yuqiao Han, Ruyue Zhang, Ruihuan Du, Zhishuo Zhou, Zhengdan Zhu, Xun Liu, Jiecheng Guo

TL;DR

This paper focuses on capturing the dataset shift from user behavior and domain distribution changing over time, and proposes an Incremental Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle this challenge.

Abstract

Causal effect estimation has been widely used in marketing optimization. The framework of an uplift model followed by a constrained optimization algorithm is popular in practice. To enhance performance in the online environment, the framework needs to be improved to address the complexities caused by temporal dataset shift. This paper focuses on capturing the dataset shift from user behavior and domain distribution changing over time. We propose an Incremental Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle this challenge. The ICE-PKD framework includes two components: (i) a multi-treatment uplift network that eliminates confounding bias using counterfactual regression; (ii) an incremental training strategy that adapts to the temporal dataset shift by updating with the latest data and protects generalization via replay-based knowledge distillation. We also revisit the uplift modeling metrics and introduce a novel metric for more precise online evaluation in multiple treatment scenarios. Extensive experiments on both simulated and online datasets show that the proposed framework achieves better performance. The ICE-PKD framework has been deployed in the marketing system of Huaxiaozhu, a ride-hailing platform in China.

The Estimation of Continual Causal Effect for Dataset Shifting Streams

TL;DR

This paper focuses on capturing the dataset shift from user behavior and domain distribution changing over time, and proposes an Incremental Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle this challenge.

Abstract

Causal effect estimation has been widely used in marketing optimization. The framework of an uplift model followed by a constrained optimization algorithm is popular in practice. To enhance performance in the online environment, the framework needs to be improved to address the complexities caused by temporal dataset shift. This paper focuses on capturing the dataset shift from user behavior and domain distribution changing over time. We propose an Incremental Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle this challenge. The ICE-PKD framework includes two components: (i) a multi-treatment uplift network that eliminates confounding bias using counterfactual regression; (ii) an incremental training strategy that adapts to the temporal dataset shift by updating with the latest data and protects generalization via replay-based knowledge distillation. We also revisit the uplift modeling metrics and introduce a novel metric for more precise online evaluation in multiple treatment scenarios. Extensive experiments on both simulated and online datasets show that the proposed framework achieves better performance. The ICE-PKD framework has been deployed in the marketing system of Huaxiaozhu, a ride-hailing platform in China.
Paper Structure (49 sections, 29 equations, 4 figures, 3 tables, 1 algorithm)

This paper contains 49 sections, 29 equations, 4 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Uplift Modeling
  4. Meta-learner-based methods.
  5. Tree-based methods.
  6. Neural network-based methods.
  7. Evaluation Metrics for Uplift Modeling
  8. Incremental Learning
  9. Parameter isolation methods.
  10. Regularization-based methods.
  11. Reply methods.
  12. Knowledge Distillation Enhanced by Auxiliary Loss Techniques
  13. Continue Causal Effect Estimation
  14. Problem Statement
  15. Methodology
  16. ...and 34 more sections

Figures (4)

  • Figure 1: Architecture of the ICE-PKD framework. The blue trapezoids denote the base model, orange bars represent the data, pink circles indicate the operators, yellow-gray rectangles correspond to the loss function. The black dashed lines illustrate the processing of thereplayed dataset, while the black solid lines represent the processing of the latest available dataset.
  • Figure 2: Response Function Visualization for $k=0$. The blue surface corresponds to $t=0$, the red surface to $t=1$, and the green surface to $t=2$.
  • Figure 3: RAS-AUCC. The red line represents the model's predicted curve, while the green line represents the straight-line benchmark. $\Delta Cost$ and $\Delta Reward$ indicate the maximum cost and the maximum reward when the largest treatment is applied to each individual.
  • Figure 4: Comparison of performance metrics across different strategies. Each subfigure illustrates a specific metric, with the horizontal axis representing different time periods $k$ and the vertical axis depicting the difference in metric values between the various strategies and the baseline (DR-CFR A).